Comparative analysis of credit card fraud detection in Simulated Annealing trained Artificial Neural Network and Hierarchical Temporal Memory

• Main Authors: E.N. Osegi, E.F. Jumbo
• Format: Article
• Language: English
• Published: Elsevier 2021-12-01
• Series: Machine Learning with Applications
• Subjects: Hierarchical Temporal Memory; Artificial Neural Network; Simulated Annealing Algorithm; Cortical Learning Algorithm; Misclassification; Sparse distributed representation
• Online Access: http://www.sciencedirect.com/science/article/pii/S2666827021000402

The problem of misclassification has always been a major concern in detecting online credit card fraud in e-commerce systems. This concern greatly poses a significant challenge to financial institutions and online merchants with regards to financial loss. This paper specifically compares an Artificial Neural Network trained by the Simulated Annealing technique (SA-ANN) with a proposed emerging online learning technology in anomaly detection known as the Hierarchical Temporal Memory based on the Cortical Learning Algorithms (HTM-CLA). Comparisons are also made with a deep recurrent neural technique based on the Long Short-Term Memory ANN (LSTM-ANN). The performances of these systems are investigated on the basis of correctly classifying credit card fraud (CCF) using an average classification performance ratio metric. The results of simulations on two CCF benchmark datasets (the Australian and German CCF data) showed promising competitive performance of the proposed HTM-CLA with the SA-ANN. The HTM-CLA also clearly outperformed the LSTM-ANN in the considered benchmark datasets by a factor of 2:1.